Today, you will load a filtered gapminder dataset - with a subset of data on global development from 1952 - 2007 in increments of 5 years - to capture the period between the Second World War and the Global Financial Crisis.
Your task: Explore the data and visualise it in both static and animated ways, providing answers and solutions to 7 questions/tasks below.
First, start with installing the relevant packages ‘tidyverse’, ‘gganimate’, and ‘gapminder’.
## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──
## ✔ ggplot2 3.3.5 ✔ purrr 0.3.4
## ✔ tibble 3.1.8 ✔ dplyr 1.0.9
## ✔ tidyr 1.1.4 ✔ stringr 1.4.0
## ✔ readr 2.1.0 ✔ forcats 0.5.1
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
First, see which specific years are actually represented in the dataset and what variables are being recorded for each country. Note that when you run the cell below, Rmarkdown will give you two results - one for each line - that you can flip between.
str(gapminder)
## tibble [1,704 × 6] (S3: tbl_df/tbl/data.frame)
## $ country : Factor w/ 142 levels "Afghanistan",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ continent: Factor w/ 5 levels "Africa","Americas",..: 3 3 3 3 3 3 3 3 3 3 ...
## $ year : int [1:1704] 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
## $ lifeExp : num [1:1704] 28.8 30.3 32 34 36.1 ...
## $ pop : int [1:1704] 8425333 9240934 10267083 11537966 13079460 14880372 12881816 13867957 16317921 22227415 ...
## $ gdpPercap: num [1:1704] 779 821 853 836 740 ...
unique(gapminder$year)
## [1] 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 2002 2007
head(gapminder)
## # A tibble: 6 × 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Afghanistan Asia 1952 28.8 8425333 779.
## 2 Afghanistan Asia 1957 30.3 9240934 821.
## 3 Afghanistan Asia 1962 32.0 10267083 853.
## 4 Afghanistan Asia 1967 34.0 11537966 836.
## 5 Afghanistan Asia 1972 36.1 13079460 740.
## 6 Afghanistan Asia 1977 38.4 14880372 786.
The dataset contains information on each country in the sampled year, its continent, life expectancy, population, and GDP per capita.
Let’s plot all the countries in 1952.
theme_set(theme_bw()) # set theme to white background for better visibility
ggplot(subset(gapminder, year == 1952), aes(gdpPercap, lifeExp, size = pop)) +
geom_point() +
scale_x_log10()
…
We see an interesting spread with an outlier to the right. Answer the following questions, please:
gapminder %>%
filter(year==1952) %>% # We start of by filtering the data, so it only includes data from the year 1952
group_by(country) %>% # We then group the data after country, since it is the country outlier, that we have to find
summarize(gdpPercap) %>% # We then summarize over gdp per capita, to see the values for the different countries
arrange(desc(gdpPercap)) %>% # We then arrange it in a descending order, so we get the richest country first, which would have to be the outlier
head(n = 10) # We see the first 10 countries, as the addtional countries can be used for comparison to the outlier
## # A tibble: 10 × 2
## country gdpPercap
## <fct> <dbl>
## 1 Kuwait 108382.
## 2 Switzerland 14734.
## 3 United States 13990.
## 4 Canada 11367.
## 5 New Zealand 10557.
## 6 Norway 10095.
## 7 Australia 10040.
## 8 United Kingdom 9980.
## 9 Bahrain 9867.
## 10 Denmark 9692.
The richest country in 1952 was Kuwait - Kuwait is therefore the outlier
Next, you can generate a similar plot for 2007 and compare the differences
ggplot(subset(gapminder, year == 2007), aes(gdpPercap, lifeExp, size = pop)) +
geom_point() +
scale_x_log10()
…
The black bubbles are a bit hard to read, the comparison would be easier with a bit more visual differentiation.
Tasks:
options(scipen=10000) # Eliminating the scientific notation
ggplot(subset(gapminder, year == 2007), aes(gdpPercap,lifeExp, size = pop, color = continent)) + # We set up the ggplot object, and set 'color' to be continent, so we can differentiate the continents by color.
geom_point() +
ylab("Life expectancy (years)")+ # Making the axes labels more legible
xlab("GDP per capita (constant 2011 international $)") +
labs(color = "Continent", size = "Population size") + # Making the legend more legible
scale_x_log10()+ # Scaling the x-axis to be on the log10 scale
scale_y_continuous(n.breaks = 15) # Make some more ticks on the y-axis to make the figure more legible
gapminder %>%
filter(year==2007) %>% # First off, we filter the data, so it only includes data from the year 2007
group_by(country) %>% # We then group the data by country, since it is the five richest *countries* we need to find
summarize(gdpPercap) %>% # Summarize by gdpPercap, since it is the five *richest* countries we need to find
arrange(desc(gdpPercap)) %>% # Arranging the countries, so it is the richest to poorest
head(n = 5) # Taking the top five countries, which would be the five richest countries
## # A tibble: 5 × 2
## country gdpPercap
## <fct> <dbl>
## 1 Norway 49357.
## 2 Kuwait 47307.
## 3 Singapore 47143.
## 4 United States 42952.
## 5 Ireland 40676.
The five richest countries in the world in 2007, in an ascending order, is: Norway, Kuwait, Singapore, United States and Ireland
The comparison would be easier if we had the two graphs together,
animated. We have a lovely tool in R to do this: the
gganimate package. Beware that there may be other packages
your operating system needs in order to glue interim images into an
animation or video. Read the messages when installing the package.
Also, there are two ways of animating the gapminder ggplot.
The first step is to create the object-to-be-animated
anim <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop)) +
geom_point() +
scale_x_log10() # convert x to log scale
anim
…
This plot collates all the points across time. The next step is to
split it into years and animate it. This may take some time, depending
on the processing power of your computer (and other things you are
asking it to do). Beware that the animation might appear in the bottom
right ‘Viewer’ pane, not in this rmd preview. You need to
knit the document to get the visual inside an html
file.
anim + transition_states(year,
transition_length = 1,
state_length = 1)
…
Notice how the animation moves jerkily, ‘jumping’ from one year to the next 12 times in total. This is a bit clunky, which is why it’s good we have another option.
This option smoothes the transition between different ‘frames’, because it interpolates and adds transitional years where there are gaps in the timeseries data.
anim2 <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop)) +
geom_point() +
scale_x_log10() + # convert x to log scale
transition_time(year)
anim2
The much smoother movement in Option 2 will be much more noticeable if you add a title to the chart, that will page through the years corresponding to each frame.
Now, choose one of the animation options and get it to work. You may
need to troubleshoot your installation of gganimate and
other packages
Can you add a title to one or both of the animations above
that will change in sync with the animation?
(Hint: search labeling for
transition_states() and transition_time()
functions respectively)
Can you made the axes’ labels and units more readable? Consider expanding the abreviated lables as well as the scientific notation in the legend and x axis to whole numbers.
anim3 <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop, color = continent)) +
geom_point() + # We want to represent the data in a scatterplot
scale_x_log10() + # convert x to log scale
transition_time(year)+ # Make the animation
enter_fade() +
exit_shrink() +
ease_aes('linear') +
labs(title = 'Year: {round(frame_time,0)}') + # Adding a title that is sync with the animation
ylab("Life expectancy (years)") + # Adding some nice axes labels
xlab("GDP per capita (constant 2011 international $)") +
labs(color = "Continent", size = "Population size") + # Make the legend labels nice as well
scale_y_continuous(n.breaks = 15) # Adding some extra ticks on the y-axis, to make it more legible
anim3
gapminder_unfiltered dataset and
download more at https://www.gapminder.org/data/ ]# We start off with altering the data set, so it fits our research question
my_gap = gapminder %>%
group_by(continent,year) %>% # We want to make the mean for all continents for all years
summarize(mean(lifeExp)) # And here is where we take the actual mean
## `summarise()` has grouped output by 'continent'. You can override using the
## `.groups` argument.
colnames(my_gap)[3] = "mean_life_exp" # Rename the column, so we can work with it more easily
skrrt_animate = ggplot(my_gap, aes(continent,mean_life_exp, fill = continent))+ # We set up the animation, make it so that the continents are on the x-axis and mean life expectancies are on the y-axis
geom_col() + # We want the data represented by columns
transition_time(year) + # Making the animation
labs(title = 'Year: {round(frame_time,0)}') + # Adding a title that is in sync with the animation
ylab("Mean Life expectancy (years)") + # Adding some more nice labels for the axes
xlab("Continent") +
theme(legend.position="none") # The legend is redundant, so we remove it (it just denotes the same stuff that the x-axis does)
skrrt_animate
My data visualization answers the question in terms of that it
illustrates the development of the mean life expectancies of the
continents over the years. We see that the general trend for the
continents, is that the mean life expectancy steadily increases for them
all - the difference is that some countries develop much faster, such
Asia, whereas the one for Africa actually stagnates around year
1990-2000.